Our client, a leading multinational telecommunications technology company, has an urgent need for an Context Engineer.

Responsibilities:

•Sign and implement the multi-regional KB architecture in P1 alongside SA.
•Seed the KB across 12 Verizon regions during P2 (cohort 1 wk1, cohort 2 wk2, cohort 3 wk3 of July).
•Build and operate the ingestion pipeline (machine-readable regional standards → embeddings → retrievable patterns) with sampled human approval gate.
•Add MOD-specific retrievable context as regional overlays during Sep–Oct.
•Own KB integrity checks, retrieval evaluation, and weekly health reporting.

If you would like to learn more, please reach out to Elite Technical.

Required Skills

Required Skills:

•4–6 yrs hands-on RAG / retrieval / vector store engineering in production.
•Built a multi-tenant or multi-region retrieval architecture with overlay / inheritance semantics, not just "one big index."
•Vertex AI Vector Search OR transferable depth (Pinecone, Weaviate, pgvector with strong tenancy, OpenSearch hybrid).
•Embedding model evaluation discipline — retrieval quality metrics (recall@k, precision@k, MRR), not vibes.
•Python; familiar with structured-doc ingestion pipelines (PDF / XML / spreadsheet → chunked, normalised, embedded).